{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.metrics import pairwise, auc, roc_curve\n",
    "from sklearn.preprocessing import normalize\n",
    "import numpy as np, pandas as pd, scipy.sparse as sparse\n",
    "from matplotlib import pyplot as plt, font_manager as mfm, os\n",
    "import sys \n",
    "sys.path.append('..')\n",
    "import helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_row = 5000\n",
    "validate_row = 5348\n",
    "label_prefix = '__label__'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>publisher</th>\n",
       "      <th>category</th>\n",
       "      <th>story</th>\n",
       "      <th>hostname</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>main_content</th>\n",
       "      <th>main_content_len</th>\n",
       "      <th>category_publish_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>article_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43284</th>\n",
       "      <td>43284</td>\n",
       "      <td>Hercules Trailer &amp; Chat With The Rock</td>\n",
       "      <td>http://www.ign.com/articles/2014/03/25/dwayne-...</td>\n",
       "      <td>IGN</td>\n",
       "      <td>e</td>\n",
       "      <td>dTbKK7l244Hp6HM7207mWyA2KJgVM</td>\n",
       "      <td>www.ign.com</td>\n",
       "      <td>1395779126415</td>\n",
       "      <td>Share. Trailer for the Rock's Greek epic scree...</td>\n",
       "      <td>21168.0</td>\n",
       "      <td>entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41595</th>\n",
       "      <td>41595</td>\n",
       "      <td>Payday Loans Can Cost Borrowers Too Much</td>\n",
       "      <td>http://www.moneynews.com/Personal-Finance/payd...</td>\n",
       "      <td>Moneynews</td>\n",
       "      <td>b</td>\n",
       "      <td>doiNFEeUqSOVKfMsXO2REYlBVJEwM</td>\n",
       "      <td>www.moneynews.com</td>\n",
       "      <td>1395770655509</td>\n",
       "      <td>About half of all payday loans are made to peo...</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21202</th>\n",
       "      <td>21202</td>\n",
       "      <td>GM admits dragging feet on recall; 12 deaths t...</td>\n",
       "      <td>http://triblive.com/business/headlines/5789422...</td>\n",
       "      <td>Tribune-Review</td>\n",
       "      <td>b</td>\n",
       "      <td>dZEBEqzKdqmK4lMXyRNjrLYWM_4PM</td>\n",
       "      <td>triblive.com</td>\n",
       "      <td>1395231169119</td>\n",
       "      <td>DETROIT — The top executive of General Motors ...</td>\n",
       "      <td>3444.0</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14390</th>\n",
       "      <td>14390</td>\n",
       "      <td>Funnyman David Brenner dies at 78</td>\n",
       "      <td>http://nypost.com/2014/03/15/funnyman-david-br...</td>\n",
       "      <td>New York Post</td>\n",
       "      <td>e</td>\n",
       "      <td>dvawFYUSFkcNtCMWO8a__hUFSiv7M</td>\n",
       "      <td>nypost.com</td>\n",
       "      <td>1395062411788</td>\n",
       "      <td>LOS ANGELES — David Brenner, the lanky, toothy...</td>\n",
       "      <td>4639.0</td>\n",
       "      <td>entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4881</th>\n",
       "      <td>4881</td>\n",
       "      <td>UPDATE 1-RBS might have to leave an independen...</td>\n",
       "      <td>http://in.reuters.com/article/2014/03/11/boe-b...</td>\n",
       "      <td>Reuters</td>\n",
       "      <td>b</td>\n",
       "      <td>dBxpRXe8n64NSYMS2ELgYC0Mp3ySM</td>\n",
       "      <td>in.reuters.com</td>\n",
       "      <td>1394573581368</td>\n",
       "      <td>By Belinda Goldsmith and David Milliken\\n\\nLON...</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            article_id                                              title  \\\n",
       "article_id                                                                  \n",
       "43284            43284              Hercules Trailer & Chat With The Rock   \n",
       "41595            41595           Payday Loans Can Cost Borrowers Too Much   \n",
       "21202            21202  GM admits dragging feet on recall; 12 deaths t...   \n",
       "14390            14390                  Funnyman David Brenner dies at 78   \n",
       "4881              4881  UPDATE 1-RBS might have to leave an independen...   \n",
       "\n",
       "                                                          url       publisher  \\\n",
       "article_id                                                                      \n",
       "43284       http://www.ign.com/articles/2014/03/25/dwayne-...             IGN   \n",
       "41595       http://www.moneynews.com/Personal-Finance/payd...       Moneynews   \n",
       "21202       http://triblive.com/business/headlines/5789422...  Tribune-Review   \n",
       "14390       http://nypost.com/2014/03/15/funnyman-david-br...   New York Post   \n",
       "4881        http://in.reuters.com/article/2014/03/11/boe-b...         Reuters   \n",
       "\n",
       "           category                          story           hostname  \\\n",
       "article_id                                                              \n",
       "43284             e  dTbKK7l244Hp6HM7207mWyA2KJgVM        www.ign.com   \n",
       "41595             b  doiNFEeUqSOVKfMsXO2REYlBVJEwM  www.moneynews.com   \n",
       "21202             b  dZEBEqzKdqmK4lMXyRNjrLYWM_4PM       triblive.com   \n",
       "14390             e  dvawFYUSFkcNtCMWO8a__hUFSiv7M         nypost.com   \n",
       "4881              b  dBxpRXe8n64NSYMS2ELgYC0Mp3ySM     in.reuters.com   \n",
       "\n",
       "                timestamp                                       main_content  \\\n",
       "article_id                                                                     \n",
       "43284       1395779126415  Share. Trailer for the Rock's Greek epic scree...   \n",
       "41595       1395770655509  About half of all payday loans are made to peo...   \n",
       "21202       1395231169119  DETROIT — The top executive of General Motors ...   \n",
       "14390       1395062411788  LOS ANGELES — David Brenner, the lanky, toothy...   \n",
       "4881        1394573581368  By Belinda Goldsmith and David Milliken\\n\\nLON...   \n",
       "\n",
       "            main_content_len category_publish_name  \n",
       "article_id                                          \n",
       "43284                21168.0         entertainment  \n",
       "41595                 1966.0              business  \n",
       "21202                 3444.0              business  \n",
       "14390                 4639.0         entertainment  \n",
       "4881                  2634.0              business  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "article_contents = pd.read_parquet('../datasets/uci_news.snappy.parquet')\n",
    "article_contents = article_contents.iloc[0:train_row + validate_row].sample(frac=1)\n",
    "article_contents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vectorizer = CountVectorizer(min_df=0, max_df=0.99, max_features=10000)\n",
    "X_train = count_vectorizer.fit_transform(article_contents.main_content.iloc[0:train_row])\n",
    "X_train = count_vectorizer.inverse_transform(X_train)\n",
    "Y_train = article_contents.category.iloc[0:train_row]\n",
    "X_validate = count_vectorizer.transform(article_contents.main_content[train_row:validate_row + train_row])\n",
    "X_validate = count_vectorizer.inverse_transform(X_validate)\n",
    "Y_validate = article_contents.category.iloc[train_row:validate_row + train_row]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"uci_train_starspace_formatted.txt\", 'w+') as file:\n",
    "    for i in range(train_row):\n",
    "        file.write(' '.join(X_train[i]) + ' ' + label_prefix + Y_train.iloc[i])\n",
    "        file.write('\\n')\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"uci_validate_starspace_formatted.txt\", 'w+') as file:\n",
    "    for i in range(validate_row):\n",
    "        file.write(' '.join(X_validate[i]) + ' ' + label_prefix + Y_validate.iloc[i])\n",
    "        file.write('\\n')\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shell command to train the model\n",
    "# starspace train -trainFile uci_train_starspace_formatted.txt -model uci_starspace -trainMode 0 -validationFile uci_validate_starspace_formatted.txt -dim 50 -epoch 50 -negSearchLimit 1 -thread 20 -lr 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shell command to get the embeddings\n",
    "# embed_doc uci_starspace uci_train_starspace_formatted.txt > uci_train_starspace_embed.txt\n",
    "# embed_doc uci_starspace uci_validate_starspace_formatted.txt > uci_validate_starspace_embed.txt\n",
    "# tail -n+4 uci_train_starspace_embed.txt | sed '1d; n; d' > uci_train_starspace_embed2.txt\n",
    "# tail -n+4 uci_validate_starspace_embed.txt | sed '1d; n; d' > uci_validate_starspace_embed2.txt\n",
    "# mv uci_train_starspace_embed2.txt uci_train_starspace_embed.txt\n",
    "# mv uci_validate_starspace_embed2.txt uci_validate_starspace_embed.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_embed = pd.read_csv('uci_train_starspace_embed.txt', sep=' ', header=None)\n",
    "X_train_embed.drop(50,1, inplace=True)\n",
    "X_validate_embed = pd.read_csv('uci_validate_starspace_embed.txt', sep=' ', header=None)\n",
    "X_validate_embed.drop(50,1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cosine_sim = helpers.pairwise_similarity(X_train_embed, metric='cosine')\n",
    "validate_cosine_sim = helpers.pairwise_similarity(X_validate_embed, metric='cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(min_df=0, max_df=0.99, max_features=10000)\n",
    "X_tfidf_train = tfidf_vectorizer.fit_transform(article_contents.main_content.iloc[0:train_row])\n",
    "X_tfidf_validate = tfidf_vectorizer.fit_transform(article_contents.main_content.iloc[train_row:validate_row + train_row])\n",
    "tfidf_train_cosine_sim = helpers.pairwise_similarity(X_tfidf_train, metric='cosine')\n",
    "tfidf_validate_cosine_sim = helpers.pairwise_similarity(X_tfidf_validate, metric='cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000,)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.factorize(Y_train)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Category Cosine Similarity')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot categorical similarity AUROC\n",
    "labels = pd.factorize(Y_train)[0]\n",
    "if labels.shape.__len__() == 1: labels = np.expand_dims(labels, 1)\n",
    "    \n",
    "not_nan_mask = np.squeeze(np.logical_and(np.expand_dims(labels, 0) >= 0, np.expand_dims(labels, 1) >= 0))\n",
    "mask = np.squeeze(np.equal(np.expand_dims(labels, 0), np.expand_dims(labels, 1)))\n",
    "mask = np.logical_and(mask, not_nan_mask)\n",
    "related_mask = sparse.coo_matrix(np.tril(mask, -1))\n",
    "unrelated_mask = sparse.coo_matrix(np.tril(np.logical_and(np.logical_not(mask), not_nan_mask), -1))\n",
    "\n",
    "# Init plot\n",
    "figsize=(16, 9)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "plt.subplot(121)\n",
    "lw = 2\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = tfidf_train_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='Tfidf (area = %0.2f)' % auroc)\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = train_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='purple', lw=lw, label='Starspace Embeddings (area = %0.2f)' % auroc)\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.title('Training Data')\n",
    "\n",
    "labels = pd.factorize(Y_validate)[0]\n",
    "if labels.shape.__len__() == 1: labels = np.expand_dims(labels, 1)\n",
    "\n",
    "not_nan_mask = np.squeeze(np.logical_and(np.expand_dims(labels, 0) >= 0, np.expand_dims(labels, 1) >= 0))\n",
    "mask = np.squeeze(np.equal(np.expand_dims(labels, 0), np.expand_dims(labels, 1)))\n",
    "mask = np.logical_and(mask, not_nan_mask)\n",
    "related_mask = sparse.coo_matrix(np.tril(mask, -1))\n",
    "unrelated_mask = sparse.coo_matrix(np.tril(np.logical_and(np.logical_not(mask), not_nan_mask), -1))\n",
    "\n",
    "# Init 2nd plot\n",
    "plt.subplot(122)\n",
    "lw = 2\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = tfidf_validate_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='Tfidf (area = %0.2f)' % auroc)\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = validate_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='purple', lw=lw, label='Starspace Embeddings (area = %0.2f)' % auroc)\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.title('Testing Data')\n",
    "\n",
    "fig.suptitle(\"Category Cosine Similarity\", fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Story Cosine Similarity')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot categorical similarity AUROC\n",
    "labels = pd.factorize(article_contents.story.iloc[0:train_row])[0]\n",
    "if labels.shape.__len__() == 1: labels = np.expand_dims(labels, 1)\n",
    "    \n",
    "not_nan_mask = np.squeeze(np.logical_and(np.expand_dims(labels, 0) >= 0, np.expand_dims(labels, 1) >= 0))\n",
    "mask = np.squeeze(np.equal(np.expand_dims(labels, 0), np.expand_dims(labels, 1)))\n",
    "mask = np.logical_and(mask, not_nan_mask)\n",
    "related_mask = sparse.coo_matrix(np.tril(mask, -1))\n",
    "unrelated_mask = sparse.coo_matrix(np.tril(np.logical_and(np.logical_not(mask), not_nan_mask), -1))\n",
    "\n",
    "# Init plot\n",
    "figsize=(16, 9)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "plt.subplot(121)\n",
    "lw = 2\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = tfidf_train_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='Tfidf (area = %0.2f)' % auroc)\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = train_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='purple', lw=lw, label='Starspace Embeddings (area = %0.2f)' % auroc)\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.title('Training Data')\n",
    "\n",
    "labels = pd.factorize(article_contents.story.iloc[train_row:train_row + validate_row])[0]\n",
    "if labels.shape.__len__() == 1: labels = np.expand_dims(labels, 1)\n",
    "\n",
    "not_nan_mask = np.squeeze(np.logical_and(np.expand_dims(labels, 0) >= 0, np.expand_dims(labels, 1) >= 0))\n",
    "mask = np.squeeze(np.equal(np.expand_dims(labels, 0), np.expand_dims(labels, 1)))\n",
    "mask = np.logical_and(mask, not_nan_mask)\n",
    "related_mask = sparse.coo_matrix(np.tril(mask, -1))\n",
    "unrelated_mask = sparse.coo_matrix(np.tril(np.logical_and(np.logical_not(mask), not_nan_mask), -1))\n",
    "\n",
    "# Init 2nd plot\n",
    "plt.subplot(122)\n",
    "lw = 2\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = tfidf_validate_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='Tfidf (area = %0.2f)' % auroc)\n",
    "\n",
    "# AUROC\n",
    "pairwise_similarity_metrics = validate_cosine_sim\n",
    "related_data = pairwise_similarity_metrics[related_mask.row, related_mask.col]\n",
    "unrelated_data = pairwise_similarity_metrics[unrelated_mask.row, unrelated_mask.col]\n",
    "fpr, tpr, thresholds = roc_curve(['Related']*len(related_data) + ['Unrelated']*len(unrelated_data), list(related_data) + list(unrelated_data), pos_label='Related')\n",
    "auroc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='purple', lw=lw, label='Starspace Embeddings (area = %0.2f)' % auroc)\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.title('Testing Data')\n",
    "\n",
    "fig.suptitle(\"Story Cosine Similarity\", fontsize=14)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
